{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import tensorflow as tf\r\n",
    "import cv2\r\n",
    "from PIL import Image\r\n",
    "from tensorflow import keras\r\n",
    "import numpy as np\r\n",
    "import os"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'   #指定第一块GPU可用\r\n",
    "config=tf.compat.v1.ConfigProto()\r\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.9  # 程序最多只能占用指定gpu50%的显存\r\n",
    "config.gpu_options.allow_growth = True      #程序按需申请内存\r\n",
    "sess=tf.compat.v1.Session(config=config)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\r\n",
    "from tensorflow.keras.mixed_precision import experimental as mixed_precision\r\n",
    "#混合精度训练设置，gpu使用 “mixed_float16” tpu使用“mixed_bfloat16”\r\n",
    "policy = mixed_precision.Policy('mixed_float16')\r\n",
    "mixed_precision.set_policy(policy)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "INFO:tensorflow:Mixed precision compatibility check (mixed_float16): OK\n",
      "Your GPU will likely run quickly with dtype policy mixed_float16 as it has compute capability of at least 7.0. Your GPU: GeForce RTX 2070, compute capability 7.5\n",
      "WARNING:tensorflow:From D:\\anaconda3\\envs\\tensorflow240\\lib\\site-packages\\tensorflow\\python\\keras\\mixed_precision\\loss_scale.py:56: DynamicLossScale.__init__ (from tensorflow.python.training.experimental.loss_scale) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.keras.mixed_precision.LossScaleOptimizer instead. LossScaleOptimizer now has all the functionality of DynamicLossScale\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# 启用动态图机制\r\n",
    "tf.compat.v1.disable_eager_execution()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# 读取视频帧每个time秒读取一帧\r\n",
    "def get_movie_frame(path,time):\r\n",
    "    cap = cv2.VideoCapture(path)\r\n",
    "    total_frame = cap.get(cv2.CAP_PROP_FRAME_COUNT)\r\n",
    "    frame_data = []\r\n",
    "    for i in range(10,int(total_frame//time - 10)):\r\n",
    "        cap.set(cv2.CAP_PROP_POS_FRAMES,i * time)\r\n",
    "        success, frame = cap.read()\r\n",
    "        if success:\r\n",
    "            frame_data.append(frame)\r\n",
    "            # cv2.imencode('.png',frame)[1].tofile('C:/Users/13167/Desktop/frame/test' + str(i) + '.png')\r\n",
    "    return frame_data"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# 将数组图片中的每张图的对角线抽出size * size大小的图片\r\n",
    "def get_img_data(frame_data,x_size,y_size):\r\n",
    "    data_y = []\r\n",
    "    data_x = []\r\n",
    "    h_num = int(len(frame_data[0])//(y_size * 2))\r\n",
    "    w_num = int(len(frame_data[0][0])//(x_size * 2))\r\n",
    "    while len(frame_data):\r\n",
    "        img = frame_data.pop()\r\n",
    "        for num in range(min(h_num,w_num)):\r\n",
    "            sub_img = img[num* (x_size * 2):(num + 1) * (x_size * 2),num * (y_size * 2):(num + 1) * (y_size * 2),:]\r\n",
    "            data_y.append(sub_img)\r\n",
    "            data_x.append(cv2.resize(sub_img,[y_size,x_size]))\r\n",
    "    return data_x,data_y"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# Image.fromarray(frame_data.pop()[1 * 512:(1 + 1) * 512,1* 512:(1 + 1) * 512])\r\n",
    "# Image.fromarray(cv2.resize(frame_data.pop()[1 * 512:(1 + 1) * 512,1* 512:(1 + 1) * 512],[256,256]))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "x_size = 128\r\n",
    "y_size = 120\r\n",
    "video_frame_num = 24\r\n",
    "movie_path = \"C:/迅雷下载/哪吒之魔童降.mkv\"\r\n",
    "video_output_path = r'C:/Users/13167/Desktop/frame/test.mp4'"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "data_x , data_y = get_img_data(get_movie_frame(movie_path,2400),x_size,y_size)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "def read_img():\r\n",
    "    # 数据读取 高分辨率图片\r\n",
    "    data_x = []\r\n",
    "    data_y = []\r\n",
    "    path = '../train/train'\r\n",
    "    photo_num = 0\r\n",
    "    y_size = 256\r\n",
    "    x_size = 128\r\n",
    "\r\n",
    "    for fileName in os.listdir(path):\r\n",
    "        if photo_num < 300:\r\n",
    "            print('processing[%d]:[%s]' %(photo_num,fileName))\r\n",
    "            img = cv2.imread(path + '/' + fileName)\r\n",
    "            patch = img.shape[0]//y_size\r\n",
    "            for num in range(patch):\r\n",
    "                data_y.append(img[num * y_size:(num + 1) * y_size,num* y_size:(num + 1) * y_size])\r\n",
    "                data_x.append(cv2.resize(img[num * y_size:(num + 1) * y_size,num* y_size:(num + 1) * y_size],[x_size,x_size]))\r\n",
    "            photo_num = photo_num + 1\r\n",
    "    return data_x,data_y"
   ],
   "outputs": [],
   "metadata": {
    "scrolled": true,
    "tags": []
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "def soft_dice_loss(y_true, y_pred, epsilon=1e-7):\r\n",
    "    y_true = tf.cast(y_true,'float32')\r\n",
    "    y_pred = tf.cast(y_pred,'float32')\r\n",
    "    axes = tuple(range(1, len(y_pred.shape) - 1))\r\n",
    "    MSE = tf.keras.losses.MSE(y_pred=y_pred,y_true=y_true)\r\n",
    "    numerator = 2. * tf.reduce_sum(y_pred * y_true, axes)\r\n",
    "    denominator = tf.reduce_sum(tf.square(y_pred) + tf.square(y_true), axes)\r\n",
    "    return MSE+(1 - tf.reduce_mean(numerator / (denominator + epsilon)))\r\n",
    "#     return MSE"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "def mse_and_l1(y_pred, y_true):\r\n",
    "        y_true = tf.cast(y_true,'float32')\r\n",
    "        y_pred = tf.cast(y_pred,'float32')\r\n",
    "        MSE = tf.keras.losses.MSE(y_pred=y_pred,y_true=y_true)\r\n",
    "        diff=tf.abs(y_true-y_pred)\r\n",
    "        less_than_one=tf.cast(tf.less(diff,1.0),'float32')\r\n",
    "        smooth_l1_loss=(less_than_one*0.5*diff**2)+(1.0-less_than_one)*(diff-0.5)\r\n",
    "        return tf.reduce_mean(smooth_l1_loss) + MSE"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "def psnr(y_pred, y_true):\r\n",
    "    return tf.image.psnr(y_pred, y_true, max_val=1.0)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "def filter_block(input,filters = 16,kernel_size_list = [1,3,5]):\r\n",
    "    layer_list = []\r\n",
    "    for kernel_size in kernel_size_list:\r\n",
    "        layer_list.append(tf.keras.layers.Conv2D(filters=filters, kernel_size=kernel_size, padding='same',activation=tf.nn.relu)(input))\r\n",
    "    con_cat = tf.keras.layers.concatenate(layer_list)\r\n",
    "    output = tf.keras.layers.Conv2D(filters=filters, kernel_size=3, padding='same',activation=tf.nn.relu)(con_cat)\r\n",
    "    return output"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "def ssim_loss(y_pred, y_true):\r\n",
    "    return (1 - tf.reduce_mean(tf.image.ssim(y_true, y_pred, max_val=1.0))) * mse_and_l1(y_true, y_pred)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "def ssim(y_pred, y_true):\r\n",
    "    return tf.reduce_mean(tf.image.ssim(y_true, y_pred, max_val=1.0))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "def ssim_mul(y_pred, y_true):\r\n",
    "    return tf.reduce_mean(tf.image.ssim_multiscale(y_true, y_pred, max_val=1.0))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "def mix_ssim_loss(y_pred, y_true):\r\n",
    "    y_true = tf.cast(y_true,'float32')\r\n",
    "    y_pred = tf.cast(y_pred,'float32')\r\n",
    "    return 2 - tf.reduce_mean(tf.image.ssim(y_true, y_pred, max_val=1.0)) - tf.reduce_mean(tf.image.ssim_multiscale(y_true, y_pred, max_val=1.0))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "# def scheduler(epoch):\r\n",
    "#     if epoch % 100:\r\n",
    "#         keras.backend.set_value(keras.backend.get_value() * 0.5)\r\n",
    "#     return model.lr.get_value()\r\n",
    "# change_lr = keras.callbacks.LearningRateScheduler(scheduler)\r\n",
    "\r\n",
    "\r\n",
    "reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='loss', patience=10, mode='auto',cooldown=0,factor=0.9,verbose=1)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "def super_pixel():\r\n",
    "    input_xs = tf.keras.Input(shape=[x_size,y_size,3])\r\n",
    "    conv_99 = tf.keras.layers.Conv2D(filters=32,kernel_size=3,padding='same',activation=tf.nn.relu)(input_xs)\r\n",
    "    conv_0 = tf.keras.layers.Conv2D(filters=32,kernel_size=3,padding='same',activation=tf.nn.relu)(conv_99)\r\n",
    "    conv_0 = tf.keras.layers.Conv2D(filters=64,kernel_size=3,padding='same',activation=tf.nn.relu)(conv_0)\r\n",
    "    uppool_0 = tf.keras.layers.UpSampling2D()(conv_0)\r\n",
    "    convT_0 = tf.keras.layers.Conv2DTranspose(filters=64, kernel_size=2, padding='same',activation=tf.nn.relu)(uppool_0)\r\n",
    "    conv_1 = tf.keras.layers.Conv2D(filters=64,kernel_size=3,padding='same',activation=tf.nn.relu)(convT_0)\r\n",
    "    conv_1 = tf.keras.layers.Conv2D(filters=64,kernel_size=3,padding='same',activation=tf.nn.relu)(conv_1)\r\n",
    "    conv_1 = tf.keras.layers.BatchNormalization()(conv_1)\r\n",
    "    pool_1 = tf.keras.layers.MaxPool2D(pool_size=(2, 2), padding='same')(conv_1)\r\n",
    "    conv_2 = tf.keras.layers.Conv2D(filters=128, kernel_size=3, padding='same',activation=tf.nn.relu)(pool_1)\r\n",
    "    conv_2 = tf.keras.layers.Conv2D(filters=128, kernel_size=3, padding='same',activation=tf.nn.relu)(conv_2)\r\n",
    "    conv_2 = tf.keras.layers.Conv2D(filters=128, kernel_size=3, padding='same', activation=tf.nn.relu)(conv_2)\r\n",
    "    conv_2 = tf.keras.layers.BatchNormalization()(conv_2)\r\n",
    "    pool_2 = tf.keras.layers.MaxPool2D(pool_size=(2, 2), padding='same')(conv_2)\r\n",
    "    conv_3 = tf.keras.layers.Conv2D(filters=256, kernel_size=3, padding='same',activation=tf.nn.relu)(pool_2)\r\n",
    "    conv_3 = tf.keras.layers.Conv2D(filters=256, kernel_size=3, padding='same',activation=tf.nn.relu)(conv_3)\r\n",
    "    conv_3 = tf.keras.layers.Conv2D(filters=256, kernel_size=3, padding='same', activation=tf.nn.relu)(conv_3)\r\n",
    "    conv_3 = tf.keras.layers.BatchNormalization()(conv_3)\r\n",
    "    pool_3 = tf.keras.layers.MaxPool2D(pool_size=(2, 2), padding='same')(conv_3)\r\n",
    "    conv_4 = tf.keras.layers.Conv2D(filters=512, kernel_size=3, padding='same',activation=tf.nn.relu)(pool_3)\r\n",
    "    conv_4 = tf.keras.layers.Conv2D(filters=512, kernel_size=3, padding='same', activation=tf.nn.relu)(conv_4)\r\n",
    "    conv_4 = tf.keras.layers.Conv2D(filters=512, kernel_size=3, padding='same', activation=tf.nn.relu)(conv_4)\r\n",
    "    conv_4 = tf.keras.layers.BatchNormalization()(conv_4)\r\n",
    "    uppool_6 = tf.keras.layers.UpSampling2D()(conv_4)\r\n",
    "    convT_6 = tf.keras.layers.Conv2DTranspose(filters=256,kernel_size=2,padding='same',activation=tf.nn.relu)(uppool_6)\r\n",
    "    con_6 = tf.keras.layers.concatenate([convT_6,conv_3])\r\n",
    "    filter_block_5= filter_block(con_6,filters = 256,kernel_size_list = [3,5,7])\r\n",
    "    filter_block_5= filter_block(filter_block_5,filters = 256,kernel_size_list = [3,5,7])\r\n",
    "#     conv_6 = tf.keras.layers.BatchNormalization()(filter_block_4)\r\n",
    "    uppool_7 = tf.keras.layers.UpSampling2D()(filter_block_5)\r\n",
    "    convT_7 = tf.keras.layers.Conv2DTranspose(filters=128,kernel_size=2,padding='same',activation=tf.nn.relu)(uppool_7)\r\n",
    "    con_7 = tf.keras.layers.concatenate([convT_7,conv_2])\r\n",
    "    filter_block_6 = filter_block(con_7,filters = 128,kernel_size_list = [3,4,5])\r\n",
    "    filter_block_6 = filter_block(filter_block_6,filters = 128,kernel_size_list = [3,4,5])\r\n",
    "#     conv_7 = tf.keras.layers.BatchNormalization()(conv_7)\r\n",
    "    uppool_8 = tf.keras.layers.UpSampling2D()(filter_block_6)\r\n",
    "    convT_8 = tf.keras.layers.Conv2DTranspose(filters=64, kernel_size=2, padding='same',activation=tf.nn.relu)(uppool_8)\r\n",
    "    con_8 = tf.keras.layers.concatenate([convT_8, conv_1])\r\n",
    "    filter_block_7 = filter_block(con_8,filters = 64,kernel_size_list = [1,2,3])\r\n",
    "    filter_block_8 = filter_block(filter_block_7,filters = 32,kernel_size_list = [1,2,3])\r\n",
    "#     conv_9 = tf.keras.layers.BatchNormalization()(filter_block_6)\r\n",
    "#     conv_10 = tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding='same',activation=tf.nn.relu)(conv_9)\r\n",
    "#     conv_10 = tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding='same',activation=tf.nn.relu)(conv_10)\r\n",
    "#     conv_10 = tf.keras.layers.BatchNormalization()(conv_10)\r\n",
    "    conv_10 = tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding='same',activation=tf.nn.relu)(filter_block_8)\r\n",
    "    conv_10 = tf.keras.layers.Conv2D(filters=3,kernel_size=1,padding='same',activation=tf.nn.relu)(conv_10)\r\n",
    "    model = tf.keras.Model(inputs=input_xs,outputs=conv_10)\r\n",
    "\r\n",
    "    model.summary()\r\n",
    "    return model"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "source": [
    "model = super_pixel()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 128, 120, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 128, 120, 32) 896         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 128, 120, 32) 9248        conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 128, 120, 64) 18496       conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d (UpSampling2D)    (None, 256, 240, 64) 0           conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose (Conv2DTranspo (None, 256, 240, 64) 16448       up_sampling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 256, 240, 64) 36928       conv2d_transpose[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 256, 240, 64) 36928       conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 256, 240, 64) 256         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 128, 120, 64) 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 128, 120, 128 73856       max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 128, 120, 128 147584      conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 128, 120, 128 147584      conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 128, 120, 128 512         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 64, 60, 128)  0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 64, 60, 256)  295168      max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 64, 60, 256)  590080      conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 64, 60, 256)  590080      conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 64, 60, 256)  1024        conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 32, 30, 256)  0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 32, 30, 512)  1180160     max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 32, 30, 512)  2359808     conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 32, 30, 512)  2359808     conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 32, 30, 512)  2048        conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, 64, 60, 512)  0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 64, 60, 256)  524544      up_sampling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 64, 60, 512)  0           conv2d_transpose_1[0][0]         \n",
      "                                                                 batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 64, 60, 256)  1179904     concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 64, 60, 256)  3277056     concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 64, 60, 256)  6422784     concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 64, 60, 768)  0           conv2d_14[0][0]                  \n",
      "                                                                 conv2d_15[0][0]                  \n",
      "                                                                 conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 64, 60, 256)  1769728     concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 64, 60, 256)  590080      conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 64, 60, 256)  1638656     conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 64, 60, 256)  3211520     conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 64, 60, 768)  0           conv2d_18[0][0]                  \n",
      "                                                                 conv2d_19[0][0]                  \n",
      "                                                                 conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 64, 60, 256)  1769728     concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2D)  (None, 128, 120, 256 0           conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTrans (None, 128, 120, 128 131200      up_sampling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 128, 120, 256 0           conv2d_transpose_2[0][0]         \n",
      "                                                                 batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 128, 120, 128 295040      concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 128, 120, 128 524416      concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 128, 120, 128 819328      concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 128, 120, 384 0           conv2d_22[0][0]                  \n",
      "                                                                 conv2d_23[0][0]                  \n",
      "                                                                 conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 128, 120, 128 442496      concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 128, 120, 128 147584      conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 128, 120, 128 262272      conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 128, 120, 128 409728      conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 128, 120, 384 0           conv2d_26[0][0]                  \n",
      "                                                                 conv2d_27[0][0]                  \n",
      "                                                                 conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 128, 120, 128 442496      concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2D)  (None, 256, 240, 128 0           conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTrans (None, 256, 240, 64) 32832       up_sampling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_6 (Concatenate)     (None, 256, 240, 128 0           conv2d_transpose_3[0][0]         \n",
      "                                                                 batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 256, 240, 64) 8256        concatenate_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 256, 240, 64) 32832       concatenate_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 256, 240, 64) 73792       concatenate_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_7 (Concatenate)     (None, 256, 240, 192 0           conv2d_30[0][0]                  \n",
      "                                                                 conv2d_31[0][0]                  \n",
      "                                                                 conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 256, 240, 64) 110656      concatenate_7[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 256, 240, 32) 2080        conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 256, 240, 32) 8224        conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 256, 240, 32) 18464       conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_8 (Concatenate)     (None, 256, 240, 96) 0           conv2d_34[0][0]                  \n",
      "                                                                 conv2d_35[0][0]                  \n",
      "                                                                 conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 256, 240, 32) 27680       concatenate_8[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 256, 240, 32) 9248        conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 256, 240, 3)  99          conv2d_38[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 32,049,635\n",
      "Trainable params: 32,047,715\n",
      "Non-trainable params: 1,920\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "metadata": {
    "scrolled": true,
    "tags": []
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(tf.keras.optimizers.Adam(lr=0.0001), loss_scale='dynamic')\r\n",
    "model.compile(optimizer=opt, loss=mix_ssim_loss, metrics=[psnr,ssim,ssim_mul])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WARNING:tensorflow:tf.keras.mixed_precision.experimental.LossScaleOptimizer is deprecated. Please use tf.keras.mixed_precision.LossScaleOptimizer instead. For example\n",
      "  opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "# config = tf.ConfigProto()\r\n",
    "# config.gpu_options.allow_growth = True\r\n",
    "# session = tf.Session(config=config)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "model.fit(np.array(data_x)/255.,np.array(data_y)/255.,epochs=1000,batch_size=8, verbose=2, callbacks=[reduce_lr])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Train on 96 samples\n",
      "Epoch 1/1000\n",
      "96/96 - 110s - loss: 0.8705 - psnr: 15.7402 - ssim: 0.4717 - ssim_mul: 0.6578\n",
      "Epoch 2/1000\n",
      "96/96 - 11s - loss: 0.5033 - psnr: 19.2359 - ssim: 0.6572 - ssim_mul: 0.8395\n",
      "Epoch 3/1000\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-18f0e3060023>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_x\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m255.\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m255.\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mreduce_lr\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\anaconda3\\envs\\tensorflow240\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_v1.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    787\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    788\u001b[0m     \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_select_training_loop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 789\u001b[1;33m     return func.fit(\n\u001b[0m\u001b[0;32m    790\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    791\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\tensorflow240\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_arrays_v1.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)\u001b[0m\n\u001b[0;32m    645\u001b[0m       \u001b[0mval_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_y\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_sample_weights\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 647\u001b[1;33m     return fit_loop(\n\u001b[0m\u001b[0;32m    648\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    649\u001b[0m         \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\tensorflow240\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_arrays_v1.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[1;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)\u001b[0m\n\u001b[0;32m    382\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    383\u001b[0m         \u001b[1;31m# Get outputs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 384\u001b[1;33m         \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    385\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    386\u001b[0m           \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\tensorflow240\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   3954\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeed_arrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_symbols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msymbol_vals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3955\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3956\u001b[1;33m     fetched = self._callable_fn(*array_vals,\n\u001b[0m\u001b[0;32m   3957\u001b[0m                                 run_metadata=self.run_metadata)\n\u001b[0;32m   3958\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\tensorflow240\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1478\u001b[0m       \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1479\u001b[0m         \u001b[0mrun_metadata_ptr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_NewBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1480\u001b[1;33m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[0m\u001b[0;32m   1481\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1482\u001b[0m                                                run_metadata_ptr)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "metadata": {
    "scrolled": true,
    "tags": [
     "outputPrepend"
    ]
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "# 读取视频second秒中的连续帧\r\n",
    "def get_second_frame(movid_path,start_time,end_time):\r\n",
    "    cap = cv2.VideoCapture(movid_path)\r\n",
    "    movie_frame_rate = cap.get(cv2.CAP_PROP_FPS)\r\n",
    "    start_frame = int(start_time * movie_frame_rate)\r\n",
    "    end_fream = int(start_frame + (end_time - start_time) * movie_frame_rate)\r\n",
    "    frame_data = []\r\n",
    "    for i in range(start_frame,end_fream):\r\n",
    "        cap.set(cv2.CAP_PROP_POS_FRAMES,i)\r\n",
    "        success, frame = cap.read()\r\n",
    "        if success:\r\n",
    "            frame_data.append(frame)\r\n",
    "    return frame_data"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "# 将图像数组拼接成单个图像\r\n",
    "def conbime_img(second_frame,height,width):\r\n",
    "    img = np.zeros((height,width,3))\r\n",
    "    img_height = second_frame.shape[1]\r\n",
    "    img_width = second_frame.shape[2]\r\n",
    "    index = 0\r\n",
    "    for w in range(height//img_height):\r\n",
    "        for h in range(width//img_width):\r\n",
    "            img[w * img_height:(w + 1) * img_height,h * img_width:(h + 1) * img_width,:] = second_frame[index]\r\n",
    "            index = index + 1\r\n",
    "    return img.astype(np.uint8)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "# 将一帧的图像按照size*size的大小分割\r\n",
    "def split_frame_data(frame,x_size,y_size):\r\n",
    "    split_img_list = []\r\n",
    "    img_height_num = int(len(frame)//y_size)\r\n",
    "    img_width_num = int(len(frame[0])//x_size)\r\n",
    "    for h in range(img_height_num):\r\n",
    "        for w in range(img_width_num):\r\n",
    "            split_img_list.append(frame[h * y_size:(h + 1) * y_size,w * x_size:(w + 1) * x_size,:])\r\n",
    "    return split_img_list"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "source": [
    "# 将图像数组保存成视频\r\n",
    "def save_video(video_output_path,video_frame,video_frame_num):\r\n",
    "    videowrite = cv2.VideoWriter(video_output_path,-1,video_frame_num,(len(video_frame[0][0]),len(video_frame[0])))\r\n",
    "    for i in range(len(video_frame)):\r\n",
    "        videowrite.write(video_frame[i])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "source": [
    "start_time = 1288\r\n",
    "end_time = 1289\r\n",
    "frame_list = get_second_frame(movie_path,start_time,end_time)\r\n",
    "predict_x = split_frame_data(frame_list[0],y_size,x_size)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "source": [
    "result_list = model.predict(np.array(predict_x)/.255)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "source": [
    "np.array(data_y).shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(96, 256, 240, 3)"
      ]
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "source": [
    "frame_predict_list = [] \r\n",
    "frame_predict_list.append(conbime_img(np.array(data_y) * 255,1080 * 2,1920 * 2))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "source": [
    "# Image.fromarray((frame_predict_list[0]))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "source": [
    "# Image.fromarray((frame_list[1]))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "source": [
    "np.array(predict_x).shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(128, 128, 120, 3)"
      ]
     },
     "metadata": {},
     "execution_count": 70
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "source": [],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "metadata": {},
     "execution_count": 56
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "source": [
    "img_num = 20\r\n",
    "move_index = 33\r\n",
    "result = model.predict((np.array(data_x)/255.)[move_index:move_index + img_num,:,:,:])"
   ],
   "outputs": [
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "Error when checking input: expected input_1 to have shape (128, 128, 3) but got array with shape (256, 256, 3)",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-34-b413789fa309>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mimg_num\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmove_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m33\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m255.\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmove_index\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mmove_index\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mimg_num\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\anaconda3\\envs\\tensorflow240\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_v1.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m    980\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    981\u001b[0m     \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_select_training_loop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 982\u001b[1;33m     return func.predict(\n\u001b[0m\u001b[0;32m    983\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    984\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\tensorflow240\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_arrays_v1.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, model, x, batch_size, verbose, steps, callbacks, **kwargs)\u001b[0m\n\u001b[0;32m    702\u001b[0m               **kwargs):\n\u001b[0;32m    703\u001b[0m     \u001b[0mbatch_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_or_infer_batch_size\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 704\u001b[1;33m     x, _, _ = model._standardize_user_data(\n\u001b[0m\u001b[0;32m    705\u001b[0m         x, check_steps=True, steps_name='steps', steps=steps)\n\u001b[0;32m    706\u001b[0m     return predict_loop(\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\tensorflow240\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_v1.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[1;34m(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)\u001b[0m\n\u001b[0;32m   2328\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2329\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2330\u001b[1;33m     return self._standardize_tensors(\n\u001b[0m\u001b[0;32m   2331\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2332\u001b[0m         \u001b[0mrun_eagerly\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrun_eagerly\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\tensorflow240\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_v1.py\u001b[0m in \u001b[0;36m_standardize_tensors\u001b[1;34m(self, x, y, sample_weight, run_eagerly, dict_inputs, is_dataset, class_weight, batch_size)\u001b[0m\n\u001b[0;32m   2356\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdataset_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDatasetV1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdataset_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDatasetV2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2357\u001b[0m       \u001b[1;31m# TODO(fchollet): run static checks with dataset output shape(s).\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2358\u001b[1;33m       x = training_utils_v1.standardize_input_data(\n\u001b[0m\u001b[0;32m   2359\u001b[0m           \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2360\u001b[0m           \u001b[0mfeed_input_names\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\tensorflow240\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_utils_v1.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[1;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[0;32m    535\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mdim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mref_dim\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_shape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    536\u001b[0m           \u001b[1;32mif\u001b[0m \u001b[0mref_dim\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mdim\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mref_dim\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mdim\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 537\u001b[1;33m             raise ValueError('Error when checking ' + exception_prefix +\n\u001b[0m\u001b[0;32m    538\u001b[0m                              \u001b[1;34m': expected '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' to have shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    539\u001b[0m                              \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' but got array with shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Error when checking input: expected input_1 to have shape (128, 128, 3) but got array with shape (256, 256, 3)"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "source": [
    "img_index = 8"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "source": [
    "Image.fromarray((result[img_index] * 255).reshape(256,256,3).astype(np.uint8))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=256x256 at 0x2206A2B7130>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "source": [
    "Image.fromarray(data_y[move_index + img_index])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=256x256 at 0x2206A2C4940>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "source": [
    "Image.fromarray(data_x[move_index + img_index]).resize([256,256])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=256x256 at 0x220047DC070>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "execution_count": 33
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.0 64-bit ('tensorflow240': conda)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  },
  "interpreter": {
   "hash": "e5745feb8c713e9013fc104c00442bebd6c77053c2bb0b404f5dc5a8f1c3868c"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}